{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768, 9)\n",
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6      148             72             35        0  33.6   \n",
      "1            1       85             66             29        0  26.6   \n",
      "2            8      183             64              0        0  23.3   \n",
      "3            1       89             66             23       94  28.1   \n",
      "4            0      137             40             35      168  43.1   \n",
      "\n",
      "   DiabetesPedigreeFunction  Age  Outcome  \n",
      "0                     0.627   50        1  \n",
      "1                     0.351   31        0  \n",
      "2                     0.672   32        1  \n",
      "3                     0.167   21        0  \n",
      "4                     2.288   33        1  \n",
      "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
      "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
      "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
      "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
      "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
      "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
      "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
      "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
      "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
      "\n",
      "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
      "count  768.000000                768.000000  768.000000  768.000000  \n",
      "mean    31.992578                  0.471876   33.240885    0.348958  \n",
      "std      7.884160                  0.331329   11.760232    0.476951  \n",
      "min      0.000000                  0.078000   21.000000    0.000000  \n",
      "25%     27.300000                  0.243750   24.000000    0.000000  \n",
      "50%     32.000000                  0.372500   29.000000    0.000000  \n",
      "75%     36.600000                  0.626250   41.000000    1.000000  \n",
      "max     67.100000                  2.420000   81.000000    1.000000  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n",
      "None\n",
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#1.1 读取数据\n",
    "data=pd.read_csv('diabetes.csv')\n",
    "#1.2 数据探索\n",
    "#1.2.1数据规模\n",
    "print(data.shape)\n",
    "print(data.head())\n",
    "print(data.describe())\n",
    "print(data.info())\n",
    "print(data.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\software\\python3.6\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\s出现的词数是：\n",
      "0    500\n",
      "1    268\n",
      "Name: Outcome, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#1.2.2 单特征分布\n",
    "#1.2.2.1 目标分布的直方图\n",
    "data_y=data['Outcome']\n",
    "fig=plt.figure()\n",
    "sns.countplot(data_y)\n",
    "plt.show()\n",
    "#1.2.2.2 目标分布的散点图\n",
    "fig=plt.figure()\n",
    "plt.scatter(range(len(data_y)),data_y,color='r')\n",
    "plt.xlabel('range')\n",
    "plt.ylabel('Outcome ')\n",
    "plt.show()\n",
    "#1.2.2.3 数据剔除的阶段省略，因为没有发现异常\n",
    "#1.2.2.4 其他特征直方图\n",
    "fig=plt.figure()\n",
    "ax1=fig.add_subplot(221)\n",
    "plt.xlabel('Pregnancies')\n",
    "sns.distplot(data['Pregnancies'],bins=30,kde=True)\n",
    "ax2=fig.add_subplot(222)\n",
    "sns.distplot(data['Glucose'],bins=30,kde=True)\n",
    "ax3=fig.add_subplot(223)\n",
    "sns.distplot(data['BloodPressure'],bins=30,kde=True)\n",
    "ax4=fig.add_subplot(224)\n",
    "sns.distplot(data['SkinThickness'],bins=30,kde=True)\n",
    "plt.show()\n",
    "#1.2.2.5 类别型特征的统计\n",
    "cat_feature=['Outcome']\n",
    "for col in cat_feature:\n",
    "    print('\\s出现的词数是：')\n",
    "    print(data[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies and Age is 0.5443412284023388\n",
      "Glucose and Outcome is 0.4665813983068757\n",
      "SkinThickness and Insulin is 0.4367825701200101\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.2.3 两两特征相关系数\n",
    "col=data.columns\n",
    "data_corr=data.corr().abs()#计算corr系数\n",
    "plt.subplots(figsize=(7,5))#绘制corr图\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "plt.show()\n",
    "plt.savefig('data_corr.png')#保存\n",
    "\n",
    "corr_li=[]#排序：筛选出两两特征大于0.4\n",
    "for i in range(0,data.shape[1]):\n",
    "    for j in range(i+1,data.shape[1]):\n",
    "        if data_corr.iloc[i,j]>0.4:\n",
    "            corr_li.append([data_corr.iloc[i,j],i,j])\n",
    "sorted(corr_li,key=lambda x:-np.abs(x[0]))\n",
    "for v, i, j in corr_li:\n",
    "    print('%s and %s is %s'% (col[i],col[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从两两特征图中我们可以发现：Pregnancies 和 Age的相关性很高，可以在数据处理中进行特征冗余操作；   \n",
    "Glulose和糖料病的相关性很高，该特征应该注意"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2.确定模型\n",
    "#2.1 模型前的准备\n",
    "#2.1.1 数据分割 : 20%的数据作为测试集合\n",
    "from sklearn.model_selection import train_test_split\n",
    "train_x,test_x,train_y,test_y=train_test_split(data.drop('Outcome',axis=1),data['Outcome'],random_state=33,test_size=0.2)\n",
    "\n",
    "#2.1.2 连续性变量标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_x=StandardScaler()\n",
    "train_x=ss_x.fit_transform(train_x)\n",
    "test_x=ss_x.transform(test_x)\n",
    "\n",
    "#2.1.3 并联数据\n",
    "# new_csv=np.concatenate([train_x],axis=1)#concat连接需要用[]将连接数据包裹，且要求是DataFrame或者series结构数据\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log loss of each fold is: [0.46129644 0.46510588 0.56426612 0.44377717 0.46617809]\n",
      "cv logloss is: 0.48012474004701106\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\software\\python3.6\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#2.2 确定模型\n",
    "#2.2.1 default logistics回归模型\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "\n",
    "lr=LogisticRegression()\n",
    "loss=cross_val_score(lr,train_x,train_y,cv=5,scoring='neg_log_loss')\n",
    "print('log loss of each fold is:',-loss)\n",
    "print('cv logloss is:',-loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.2.2 正则化的Logistics及参数调节\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "#设置超参\n",
    "tuned_parameters={'penalty':['l1','l2'],'C':[0.001,0.01,0.1,1,10,100,1000]}\n",
    "# penaltys=['l1','l2']\n",
    "# Cs=[0.001,0.01,0.1,1,10,100,1000]\n",
    "lr_penalty=LogisticRegression()\n",
    "grid=GridSearchCV(lr_penalty,tuned_parameters,cv=5,scoring='neg_log_loss')\n",
    "grid.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'mean_fit_time': array([0.001401  , 0.0010015 , 0.        , 0.00032783, 0.        ,\n",
      "       0.00312486, 0.        , 0.        , 0.        , 0.00624948,\n",
      "       0.        , 0.        , 0.        , 0.        ]), 'std_fit_time': array([4.90038785e-04, 2.36791783e-06, 0.00000000e+00, 6.55651093e-04,\n",
      "       0.00000000e+00, 6.24971390e-03, 0.00000000e+00, 0.00000000e+00,\n",
      "       0.00000000e+00, 7.65401309e-03, 0.00000000e+00, 0.00000000e+00,\n",
      "       0.00000000e+00, 0.00000000e+00]), 'mean_score_time': array([0.00160136, 0.00099769, 0.0031476 , 0.        , 0.        ,\n",
      "       0.        , 0.00312734, 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.00312533, 0.        ]), 'std_score_time': array([7.99728739e-04, 1.08018089e-05, 6.29520416e-03, 0.00000000e+00,\n",
      "       0.00000000e+00, 0.00000000e+00, 6.25467300e-03, 0.00000000e+00,\n",
      "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
      "       6.25066757e-03, 0.00000000e+00]), 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
      "                   100, 1000, 1000],\n",
      "             mask=[False, False, False, False, False, False, False, False,\n",
      "                   False, False, False, False, False, False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
      "                   'l2', 'l1', 'l2', 'l1', 'l2'],\n",
      "             mask=[False, False, False, False, False, False, False, False,\n",
      "                   False, False, False, False, False, False],\n",
      "       fill_value='?',\n",
      "            dtype=object), 'params': [{'C': 0.001, 'penalty': 'l1'}, {'C': 0.001, 'penalty': 'l2'}, {'C': 0.01, 'penalty': 'l1'}, {'C': 0.01, 'penalty': 'l2'}, {'C': 0.1, 'penalty': 'l1'}, {'C': 0.1, 'penalty': 'l2'}, {'C': 1, 'penalty': 'l1'}, {'C': 1, 'penalty': 'l2'}, {'C': 10, 'penalty': 'l1'}, {'C': 10, 'penalty': 'l2'}, {'C': 100, 'penalty': 'l1'}, {'C': 100, 'penalty': 'l2'}, {'C': 1000, 'penalty': 'l1'}, {'C': 1000, 'penalty': 'l2'}], 'split0_test_score': array([-0.69314718, -0.64371675, -0.66946739, -0.52816851, -0.48618211,\n",
      "       -0.4678555 , -0.4634569 , -0.46129644, -0.46108927, -0.46088502,\n",
      "       -0.46086691, -0.4608487 , -0.46084661, -0.46084513]), 'split1_test_score': array([-0.69314718, -0.64113725, -0.67517494, -0.52518603, -0.47167402,\n",
      "       -0.47077815, -0.4642645 , -0.46510588, -0.46462609, -0.46473286,\n",
      "       -0.46468717, -0.46469927, -0.46469486, -0.46469595]), 'split2_test_score': array([-0.69314718, -0.64575466, -0.6680453 , -0.5512625 , -0.54752923,\n",
      "       -0.54301469, -0.56469007, -0.56426612, -0.56847148, -0.56834734,\n",
      "       -0.56880223, -0.56879096, -0.56883231, -0.5688357 ]), 'split3_test_score': array([-0.69314718, -0.63856226, -0.67581323, -0.50975335, -0.45503262,\n",
      "       -0.44706595, -0.4436611 , -0.44377717, -0.44397535, -0.44400066,\n",
      "       -0.44402728, -0.4440332 , -0.44403519, -0.44403656]), 'split4_test_score': array([-0.69314718, -0.64152376, -0.67221592, -0.52767402, -0.47215909,\n",
      "       -0.47104103, -0.46582556, -0.46617809, -0.46606085, -0.46612355,\n",
      "       -0.4661195 , -0.4661268 , -0.46612722, -0.46612722]), 'mean_test_score': array([-0.69314718, -0.64214833, -0.6721329 , -0.52844007, -0.48658953,\n",
      "       -0.47999943, -0.48043557, -0.48017599, -0.48089656, -0.48086932,\n",
      "       -0.48095212, -0.48095123, -0.48095869, -0.48095956]), 'std_test_score': array([0.        , 0.00243715, 0.00305426, 0.0132657 , 0.0320591 ,\n",
      "       0.03276814, 0.04293953, 0.04285084, 0.04453345, 0.04448689,\n",
      "       0.04466695, 0.04466183, 0.04467828, 0.04467945]), 'rank_test_score': array([14, 12, 13, 11, 10,  1,  3,  2,  5,  4,  7,  6,  8,  9]), 'split0_train_score': array([-0.69314718, -0.64085132, -0.66189542, -0.52529099, -0.47824019,\n",
      "       -0.47085398, -0.46684222, -0.46669378, -0.4666238 , -0.46662226,\n",
      "       -0.46662151, -0.46662149, -0.46662149, -0.46662148]), 'split1_train_score': array([-0.69314718, -0.64188719, -0.67797602, -0.52532854, -0.47970434,\n",
      "       -0.47076994, -0.46692644, -0.46678157, -0.46671608, -0.4667146 ,\n",
      "       -0.4667139 , -0.46671388, -0.46671388, -0.46671388]), 'split2_train_score': array([-0.69314718, -0.63908575, -0.66162164, -0.5135644 , -0.45564291,\n",
      "       -0.44783203, -0.44194616, -0.44184498, -0.44173184, -0.44173047,\n",
      "       -0.44172922, -0.4417292 , -0.44172919, -0.44172919]), 'split3_train_score': array([-0.69314718, -0.64333601, -0.67874616, -0.5309552 , -0.4838264 ,\n",
      "       -0.47509784, -0.4706668 , -0.47052445, -0.47044462, -0.47044314,\n",
      "       -0.47044228, -0.47044226, -0.47044225, -0.47044225]), 'split4_train_score': array([-0.69314718, -0.64131272, -0.67371601, -0.52389506, -0.47771556,\n",
      "       -0.46916638, -0.46505715, -0.46489609, -0.46482209, -0.46482048,\n",
      "       -0.46481965, -0.46481964, -0.46481963, -0.46481963]), 'mean_train_score': array([-0.69314718, -0.6412946 , -0.67079105, -0.52380684, -0.47502588,\n",
      "       -0.46674403, -0.46228775, -0.46214818, -0.46206768, -0.46206619,\n",
      "       -0.46206531, -0.4620653 , -0.46206529, -0.46206529]), 'std_train_score': array([0.        , 0.00138524, 0.00757197, 0.00566626, 0.00992575,\n",
      "       0.00965834, 0.01033362, 0.01031565, 0.01033123, 0.01033118,\n",
      "       0.01033136, 0.01033136, 0.01033136, 0.01033136])}\n",
      "0.47999943493683117\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    }
   ],
   "source": [
    "print(grid.cv_results_)\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过上述交叉验证，我们可以得到最佳的超参数的取值【注意：我们在进行评价scoring时，是根据验证集的平均得分得到的，平均得分越大，效果越好，最终我们的损失越小】  \n",
    "由于我们最佳参数不是在边缘，因此我们没有必要再进行参数扩大范围的设定  \n",
    "最优结果为：0.47999943493683117\n",
    "{'C': 0.1, 'penalty': 'l2'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "g:\\software\\python3.6\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "g:\\software\\python3.6\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 l1\n",
      "1 l2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#绘制曲线\n",
    "from matplotlib import pyplot\n",
    "test_means=grid.cv_results_['mean_test_score']\n",
    "train_means=grid.cv_results_['mean_train_score']\n",
    "test_stds=grid.cv_results_['std_test_score']\n",
    "train_stds=grid.cv_results_['std_train_score']\n",
    "\n",
    "n_C=len(tuned_parameters['C'])\n",
    "n_penalty=len(tuned_parameters['penalty'])\n",
    "test_scores=np.array(test_means).reshape(n_C,n_penalty)\n",
    "train_scores=np.array(train_means).reshape(n_C,n_penalty)\n",
    "test_stds=np.array(test_stds).reshape(n_C,n_penalty)\n",
    "train_stds=np.array(train_stds).reshape(n_C,n_penalty)\n",
    "\n",
    "x_axis=np.log10(tuned_parameters['C'])\n",
    "for i,value in enumerate(tuned_parameters['penalty']):\n",
    "    print(i,value)\n",
    "    pyplot.errorbar(x_axis,test_scores[:,i],yerr=test_stds[:,i],label=tuned_parameters['penalty'][i]+'Test')\n",
    "    pyplot.errorbar(x_axis,train_scores[:,i],yerr=train_stds[:,i],label=tuned_parameters['penalty'][i]+'Train')\n",
    "    \n",
    "    pyplot.legend()\n",
    "    pyplot.xlabel('Log(C)')\n",
    "    pyplot.ylabel('neg_log_loss')\n",
    "    pyplot.savefig('LogisticGridSearch.png')\n",
    "\n",
    "pyplot.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i k g\n",
      "f f f\n",
      "-------\n",
      "0 ikg\n",
      "1 fff\n",
      "_______\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l=['ikg','fff']\n",
    "for i,a,b in l:\n",
    "    print(i,a,b)\n",
    "print('-------')\n",
    "for i,value in enumerate(l):\n",
    "    print(i,value)\n",
    "print('_______')\n",
    "a=np.array([[1,2,3,4],[5,6,7,8]])\n",
    "a[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "           class_weight=None, cv=5, dual=False, fit_intercept=True,\n",
       "           intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
       "           n_jobs=1, penalty='l1', random_state=None, refit=True,\n",
       "           scoring='neg_log_loss', solver='liblinear', tol=0.0001,\n",
       "           verbose=0)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.2.3 带正则使用LogisticsRegressionCV实现【使用该方法的优点是能提高计算速度】\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "C=[0.001,0.01,0.1,1,10,100,1000]\n",
    "lrcv_l1=LogisticRegressionCV(Cs=C,cv=5,scoring='neg_log_loss',penalty='l1',solver='liblinear',multi_class='ovr')\n",
    "lrcv_l1.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: array([[-0.69314718, -0.66946739, -0.48617735, -0.46346022, -0.46108537,\n",
       "         -0.46086224, -0.46084582],\n",
       "        [-0.69314718, -0.67517494, -0.47167665, -0.46426177, -0.46463437,\n",
       "         -0.46468941, -0.46469077],\n",
       "        [-0.69314718, -0.6680453 , -0.54753206, -0.56469776, -0.56847709,\n",
       "         -0.56879759, -0.56883235],\n",
       "        [-0.69314718, -0.67581323, -0.45503411, -0.44365855, -0.44397699,\n",
       "         -0.444029  , -0.44403304],\n",
       "        [-0.69314718, -0.67221592, -0.47216213, -0.46582614, -0.46606956,\n",
       "         -0.46612521, -0.46612724]])}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(lrcv_l1.scores_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 【注意：】交叉验证过程中，每折交叉验证中构成的训练-验证数据集，都会把不同的模型训练一遍，因此形成的结果：横向表示模型数量；纵向表示每折数据在该模型下的性能。   \n",
    "\n",
    "### 对于多分类问题，每个类别都有一个在该超参数C值下的分类器，因此评价超参数时，需要将所有类别的分类器在该参数下的性能取平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          0       0.75      0.89      0.81        99\n",
      "          1       0.69      0.45      0.55        55\n",
      "\n",
      "avg / total       0.73      0.73      0.72       154\n",
      " \n",
      "\n",
      "****************************************\n",
      "Confusion matrix:\n",
      " [[88 11]\n",
      " [30 25]]\n"
     ]
    }
   ],
   "source": [
    "#2.2.4 使用SVM模型\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "SVC1=LinearSVC()\n",
    "SVC1.fit(train_x,train_y)\n",
    "predict_test=SVC1.predict(test_x)\n",
    "\n",
    "print('Classification report for classifier %s:\\n %s \\n'%(SVC1,metrics.classification_report(test_y,predict_test)))\n",
    "print('**'*20)\n",
    "print('Confusion matrix:\\n %s'% metrics.confusion_matrix(test_y,predict_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7272727272727273\n",
      "accuracy: 0.7337662337662337\n",
      "accuracy: 0.7402597402597403\n",
      "accuracy: 0.7337662337662337\n",
      "accuracy: 0.7402597402597403\n",
      "accuracy: 0.7272727272727273\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.5194805194805194\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用校验集估计模型的性能【也可以用网格搜索交叉验证来进行超参寻优】\n",
    "def fig_grid_point_Linear(C,train_x,train_y,test_x,test_y):\n",
    "    SVC2=LinearSVC(C=C)\n",
    "    SVC2.fit(train_x,train_y)\n",
    "    accuracy=SVC2.score(test_x,test_y)#测试评估\n",
    "    print('accuracy: {}'.format(accuracy))\n",
    "#     print('accuracy:%s'%accuracy,'***'*12)\n",
    "    return accuracy\n",
    "\n",
    "#设置超参数\n",
    "Cs=np.logspace(-3,3,7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "accuracy_s=[]\n",
    "for i ,oneC in enumerate(Cs):\n",
    "    tmp=fig_grid_point_Linear(oneC,train_x,train_y,test_x,test_y)\n",
    "    accuracy_s.append(tmp)\n",
    "x_axis=np.log10(Cs)\n",
    "pyplot.plot(x_axis,np.array(accuracy_s),'b-')\n",
    "pyplot.legend()\n",
    "pyplot.xlabel('log(Cs)')\n",
    "pyplot.ylabel('accuracy')\n",
    "pyplot.savefig('SVM.png')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析：从SVM和Logistics进行比较，因为两个模型采用的评价标准不一样，个人感觉比较意义不大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.7077922077922078\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.7337662337662337\n",
      "accuracy:0.7402597402597403\n",
      "accuracy:0.7077922077922078\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.7532467532467533\n",
      "accuracy:0.7272727272727273\n",
      "accuracy:0.6883116883116883\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.7467532467532467\n",
      "accuracy:0.6623376623376623\n",
      "accuracy:0.6883116883116883\n",
      "accuracy:0.6428571428571429\n",
      "accuracy:0.6428571428571429\n"
     ]
    }
   ],
   "source": [
    "# 2.2.5 采用RBF核的SVM\n",
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C,gamma,train_x,train_y,test_x,test_y):\n",
    "    SVC3=SVC(C=C,kernel='rbf',gamma=gamma)\n",
    "    SVC3.fit(train_x,train_y)\n",
    "    accuracy=SVC3.score(test_x,test_y)\n",
    "    print('accuracy:{}'.format(accuracy))\n",
    "    return accuracy\n",
    "\n",
    "accuracy_s=[]\n",
    "Cs=np.logspace(-2,2,5)\n",
    "gammas=np.logspace(-2,2,5)\n",
    "for i ,oneC in enumerate(Cs):\n",
    "    for j,gamma in enumerate(gammas):\n",
    "        tmp=fit_grid_point_RBF(oneC,gamma,train_x,train_y,test_x,test_y)\n",
    "        accuracy_s.append(tmp)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.64285714 0.64285714 0.73376623 0.75324675 0.74675325]\n",
      "[0.64285714 0.70779221 0.74025974 0.72727273 0.66233766]\n",
      "[0.64285714 0.64285714 0.70779221 0.68831169 0.68831169]\n",
      "[0.64285714 0.64285714 0.64285714 0.64285714 0.64285714]\n",
      "[0.64285714 0.64285714 0.64285714 0.64285714 0.64285714]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_s1=np.array(accuracy_s).reshape(len(Cs),len(gammas))\n",
    "x_axis=np.log10(Cs)\n",
    "for j,gamma in enumerate(gammas):\n",
    "    print(accuracy_s1[:,j])\n",
    "    pyplot.plot(x_axis,np.array(accuracy_s1[:,j]),label='Test - log(gamma)'+str(np.log10(gamma)))\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
